HealthNav AI

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HealthNav AI

Inspiration

When every second counts, emergency responders need to make split-second decisions about where to take a patient. We realized that even in modern cities, ambulances often lose precious time choosing between hospitals without knowing which one is best equipped for a patient’s specific condition.
That inspired us to build HealthNav AI a system that intelligently guides EMS teams to the optimal hospital based on the patient’s condition, distance, and hospital specialties.

What it does

HealthNav AI analyzes a patient’s symptoms and current location, then identifies the best hospital nearby using both distance and specialty data (e.g., cardiac, trauma, neurology).
It presents this visually on an interactive map and explains why a particular hospital was chosen, giving transparency to the AI’s decision.

How we built it

We created a full-stack web application:

Frontend (HTML, CSS, JavaScript, Leaflet):
Displays a dynamic map, chat interface, and severity toggle between “Critical” and “Stable.”
Backend (Flask, Python):
Handles requests, connects to a hospital database, and generates intelligent responses.
Database (MariaDB):
Stores hospital information, including specialties and estimated travel times.
AI Integration (OpenAI API):
Interprets patient symptoms and explains the recommendation clearly for EMS use.

We used Visual Studio Code with Live Server for frontend testing and Flask for our local backend API.

Challenges we ran into

Setting up CORs between the frontend and backend to allow cross-origin requests.
Configuring MariaDB and ensuring it communicated correctly with Flask.
Handling errors gracefully when JSON inputs or API calls failed.
Integrating the AI component while keeping the response fast enough for real-time use.

Accomplishments that we're proud of

Building a fully functional prototype that ties together mapping, AI, and real-world data.
Designing a clean, responsive UI with an intuitive chat interface.